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Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks

Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
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Technologies 2020, 8(2), 19; https://doi.org/10.3390/technologies8020019
Received: 31 January 2020 / Revised: 11 April 2020 / Accepted: 13 April 2020 / Published: 15 April 2020
(This article belongs to the Special Issue Computer Vision and Image Processing Technologies)
The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. The CNN training results show an accuracy as high as 100%, which present superior performance in all factors. On the other hand, the validation results average about 99% accuracy. The CNN algorithm has proven to be the most accurate classification tool for micro gesture recognition. View Full-Text
Keywords: computer vision; gesture recognition; hand gesture; 3D hand gesture recognition; artificial intelligence; machine learning; deep learning; convolutional neural network computer vision; gesture recognition; hand gesture; 3D hand gesture recognition; artificial intelligence; machine learning; deep learning; convolutional neural network
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Alnaim, N.; Abbod, M.; Swash, R. Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks. Technologies 2020, 8, 19.

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